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Sabeti, V.. (1402). An Improved Approach to Blind Image Steganalysis using an Overlapping Blocks Idea. فناوری آموزش, 11(2), 263-276. doi: 10.22061/jecei.2022.9241.592
V. Sabeti. "An Improved Approach to Blind Image Steganalysis using an Overlapping Blocks Idea". فناوری آموزش, 11, 2, 1402, 263-276. doi: 10.22061/jecei.2022.9241.592
Sabeti, V.. (1402). 'An Improved Approach to Blind Image Steganalysis using an Overlapping Blocks Idea', فناوری آموزش, 11(2), pp. 263-276. doi: 10.22061/jecei.2022.9241.592
Sabeti, V.. An Improved Approach to Blind Image Steganalysis using an Overlapping Blocks Idea. فناوری آموزش, 1402; 11(2): 263-276. doi: 10.22061/jecei.2022.9241.592

An Improved Approach to Blind Image Steganalysis using an Overlapping Blocks Idea

Journal of Electrical and Computer Engineering Innovations (JECEI)
دوره 11، شماره 2، مهر 2023، صفحه 263-276    اصل مقاله (895.5 K)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2022.9241.592
نویسنده
V. Sabeti*
Department of Computer Engineering, Faculty of Engineerning, Alzahra University, Tehran, Iran.
تاریخ دریافت: 21 مرداد 1401،  تاریخ بازنگری: 06 آبان 1401،  تاریخ پذیرش: 09 آبان 1401 
چکیده
Background and Objectives: Steganalysis is the study of detecting messages hidden using steganography. Most steganalysis techniques, known as blind steganalysis, focus on extracting and classifying various statistical features from images. Consequently, researchers continually seek to improve the accuracy of blind detection methods. The current study proposes a blind steganalysis technique based on overlapping blocks.
Methods: The proposed method began by decomposing the image into identically sized overlapping blocks, then extracted a feature vector from each block. Subsequently, a tree-structured hierarchical clustering technique was used to partition blocks into multiple classes based on extracted features, and a classifier was trained for each class to determine whether a block is from a cover or stego image. The block decomposition process was repeated for each test image, and a classifier was selected based on the block class to make a decision for each block. Furthermore, the majority vote rule was utilized to determine whether the test image is a cover or stego image.
Results: The proposed method was evaluated using the INRIA and BOSSbase datasets. Several parameters, including the number of block classes, feature extraction method, block size, and number and block overlapping level, affected the performance of the proposed method. The optimal block size was 64 × 64 by 32 steps, and the number of block classes was set to 16. WOW, S-UNIWARD, PQ, and nsF5 were the steganographic methods employed to evaluate the proposed method. Experimental results indicated that using overlapping instead of non-overlapping blocks increased the detection of data embedded in both the spatial and Joint Photographic Experts Group (JPEG) domains by an average of over 9%. In addition, the proposed method's accuracy in detecting the S-UNIWARD method was comparable to that of other deep learning-based steganalysis techniques.
Conclusion: The concept of using overlapping blocks improves the efficiency of blind steganalysis by providing the benefit of additional and larger blocks. One of the main advantages of the proposed method is comparable detection accuracy and less computational complexity than recent deep learning-based steganalysis techniques.
کلیدواژه‌ها
Steganalysis؛ Steganography؛ Blind Steganalysis؛ Spatial Steganograph؛ JPEG Steganography
مراجع
[1] S. Dhawan , R. Gupta, "Analysis of various data security techniques of steganography: A survey," Inf. Secur. J., 30(2): 63–87, 2021.

[2] I. J. Kadhim, P. Premaratne, P. J. Vial, B. Halloran, "Comprehensive survey of image steganography: Techniques, evaluations, and trends in future research," Neurocomputing, 335: 299–326, 2019.

[3] V. Sabeti, M. Sobhani, S. M. H. Hasheminejad, "An adaptive image steganography method based on integer wavelet transform using genetic algorithm," Comput. Electr. Eng., 99: 107809, 202.

[4] R. Atta, M. Ghanbari, "A high payload data hiding scheme based on dual tree complex wavelet transform," Optik, 226: 165786, 2021.

[5] G. Paul, I. Davidson, I. Mukherjee, S. S. Ravi, "Keyless dynamic optimal multi-bit image steganography using energetic pixels," Multimed. Tools Appl., 76(5): 7445–7471, 2017.

[6] B. T. Hammad, I. T. Ahmed, N. Jamil, "A steganalysis classification algorithm based on distinctive texture features," Symmetry, 14(2): 236, 2022.

[7] M. Dalal, M. Juneja, "Steganography and steganalysis (in digital forensics): a cybersecurity guide," Multimed. Tools Appl., 80(4): 5723–5771, 2021.

[8] S. Cho, J. Wang, C. C. J. Kuo, B. H. Cha, "Block-based image steganalysis for a multi-classifier," in Proc. 2010 IEEE International Conference on Multimedia and Expo: 1457–1462, 2010.

[9] S. Cho, B. H. Cha, J. Wang, C. C. J. Kuo, "Performance study on block-based image steganalysis," in Proc. 2011 IEEE International Symposium of Circuits and Systems (ISCAS): 2649–2652.

[10] S. Cho, B. H. Cha, M. Gawecki, J. Kuo, "Block-based image steganalysis: Algorithm and performance evaluation," J. Vis. Commun. Image Represent., 24: 846–856, 2013.

[11] X. Lin, "Steganography and steganalysis," in Introductory Computer Forensics, Springer, Cham, 557–577, 2018.

[12] M. Broda, V. Hajduk, D. Levický, "Universal statistical steganalytic method," J. Electr. Eng., 68(2): 117–124, 2017.

[13] X. Y. Luo, D. S. Wang, P. Wang, F. L. Liu, "A review on blind detection for image steganography," Signal Processing, 88(9): 2138-2157, 2008.

[14] F. G. Mohammadi, H. Sajedi, "Region based Image Steganalysis using Artificial Bee Colony," J. Vis. Commun. Image Represent., 44: 214–226, 2017.

[15] A. Dehdar, A. Keshavarz, N. Parhizgar, "Image steganalysis using modified graph clustering based ant colony optimization and random forest," Multimed. Tools Appl., 1–18, 2022.

[16] J. Chen, W. Lu, Y. Fang, X. Liu, Y. Yeung, Y. Xue, "Binary image steganalysis based on local texture pattern," J. Vis. Commun. Image Represent., 55: 149–156, 2018.

[17] R. Wang, M. Xu, X. Ping, T. Zhang, "Steganalysis of JPEG images by block texture based segmentation," Multimed. Tools Appl., 74(15): 5725–5746, 2015.

[18] G. R. Suryawanshi, S. N. Mali, "Study of effect of DCT domain steganography techniques in spatial domain for JPEG Images steganalysis," Int. J. Comput. Appl., 127(6): 16–20, 2015.

[19] G. R. Suryawanshi, S. N. Mali, "Universal steganalysis using IQM and multiclass discriminator for digital images," Int. Conf. Signal Process. Commun. Power Embed. Syst.: 877–881, 2016.

[20] J. Zhu, X. Zhao, Q. Guan, "Detecting and Distinguishing Adaptive and Non-Adaptive Steganography by Image Segmentation," Int. J. Digit. Crime Forensics, 11(1): 62–77, 2019, doi: 10.4018/IJDCF.2019010105

[21] T. Pevny, J. Fridrich, "Merging markov and DCT features for multi-class JPEG steganalysis," Secur. Steganography, Watermarking Multimed. Contents IX, 6505: 650503, 2007.

[22] C. Chen, Y. Q.  Shi, "JPEG image steganalysis utilizing both intrablock and interblock correlations," in Proc. IEEE Int. Symp. Circuits Syst.: 3029–3032, 2008.

[23] T. Pevný, P. Bas, J. Fridrich, "Steganalysis by subtractive pixel adjacency matrix," IEEE Trans. Inf. Forensics Secur., 5(2): 215-224, 2010.

[24] J. Kodovský, S. Binghamton, J. Fridrich, "Calibration revisited," in Proc. 11th ACM workshop on Multimedia and security: 63-74, 2009.

[25] J. Fridrich, J. Kodovsky, "Rich models for steganalysis of digital images," IEEE Trans. Inf. Forensics Secur., 7(3): 868–882, 2012.

[26] H. Zhang, F. Liu, Z. Song, X. Zhang, Y. Zhao, "AG-Net: An advanced general CNN model for steganalysis," IEEE Access, 10: 44116–44122, 2022.

[27] G. Xu, Y. Xu, S. Zhang, X. Xie, "SFRNet: Feature extraction-fusion steganalysis network based on squeeze-and-excitation block and RepVgg Block," Secur. Commun. Networks, 2021: 1-10, 2021.

[28] S. Wu, S. Zhong, Y. Liu, M. Liu, "CIS-Net: A novel CNN model for spatial image steganalysis via cover image suppression," arXiv preprint arXiv:1912.06540, 2019.

[29] B. Li, W. Wei, A. Ferreira, S. Tan, "ReST-Net: Diverse activation modules and parallel subnets-based CNN for spatial image steganalysis," IEEE Signal Process. Lett., 25(5): 650–654, 2018.

[30] S. Wu, S. H. Zhong, Y. Liu, "A Novel Convolutional Neural Network for Image Steganalysis with Shared Normalization," IEEE Trans. Multimed., 22(1): 256–270, 2017, doi: 10.48550/arxiv.1711.07306.

[31] J. Ye, J. Ni, Y. Yi, "Deep learning hierarchical representations for image steganalysis," IEEE Trans. Inf. Forensics Secur., 12(11): 2545–2557, 2017.

[32] G. Xu, H. Z. Wu, Y. Q. Shi, "Structural design of convolutional neural networks for steganalysis," IEEE Signal Process. Lett., 23(5): 708–712, 2016.

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